Cancer is a disease with extensive histoclinical heterogeneity. Although conventional histological and clinical features have been correlated to prognosis, the same apparent prognostic type of tumors varies widely in its responsiveness to therapy and consequent survival of the patient.
New prognostic and predictive markers, which would facilitate an individualization of therapy for each patient, are needed to accurately predict patient response to treatments, such as small molecule or biological molecule drugs, in the clinic. The problem may be solved by the identification of new parameters that could better predict the patient's sensitivity to treatment. The classification of patient samples is a crucial aspect of cancer diagnosis and treatment. The association of a patient's response to a treatment with molecular and genetic markers can open up new opportunities for treatment development in non-responding patients, or distinguish a treatment's indication among other treatment choices because of higher confidence in the efficacy. Further, the pre-selection of patients who are likely to respond well to a medicine, drug, or combination therapy may reduce the number of patients needed in a clinical study or accelerate the time needed to complete a clinical development program (M. Cockett et al., Current Opinion in Biotechnology, 11:602-609 (2000)).
The ability to determine which patients are responding to anti-angiogenesis therapies (such as VEGFR-2 modulators) or predict drug sensitivity in patients is particularly challenging because drug responses reflect not only properties intrinsic to the target cells, but also a host's metabolic properties. Efforts to use genetic information to predict or monitor drug response have primarily focused on individual genes that have broad effects, such as the multidrug resistance genes mdr1 and mrp1 (P. Sonneveld, J. Intern. Med., 247:521-534 (2000)).
The development of microarray technologies for large scale characterization of gene mRNA expression pattern has made it possible to systematically search for molecular markers and to categorize cancers into distinct subgroups not evident by traditional histopathological methods (J. Khan et al., Cancer Res., 58:5009-5013 (1998); A. A. Alizadeh et al., Nature, 403:503-511 (2000); M. Bittner et al., Nature, 406:536-540 (2000); J. Khan et al., Nature Medicine, 7(6):673-679 (2001); and T. R. Golub et al., Science, 286:531-537 (1999); U. Alon et al., P. N. A. S. USA, 96:6745-6750 (1999)). Such technologies and molecular tools have made it possible to monitor the expression level of a large number of transcripts within a cell population at any given time (see, e.g., Schena et al., Science, 270:467-470 (1995); Lockhart et al., Nature Biotechnology, 14:1675-1680 (1996); Blanchard et al., Nature Biotechnology, 14:1649 (1996); U.S. Pat. No. 5,569,588 to Ashby et al.).
Recent studies demonstrate that gene expression information generated by microarray analysis of human tumors can predict clinical outcome (L. J. van't Veer et al., Nature, 415:530-536 (2002); M. Shipp et al., Nature Medicine, 8(1):68-74 (2002); G. Glinsky et al., The Journal of Clin. Invest, 113(6):913-923 (2004)). These findings bring hope that cancer treatment will be vastly improved by better predicting and monitoring the response of individual tumors to therapy.
Needed are new and alternative methods and procedures to determine drug sensitivity or monitor response in patients to allow the development of individualized diagnostics which are necessary to treat diseases and disorders based on patient response at a molecular level.